An Intelligent Question-Answering Model over Educational Knowledge Graph for Sustainable Urban Living

Yutong Fang, Jianzhi Deng (), Fengming Zhang and Hongyan Wang
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Yutong Fang: College of Information Science and Engineering, Guilin University of Technology, Guilin 541006, China
Jianzhi Deng: College of Information Science and Engineering, Guilin University of Technology, Guilin 541006, China
Fengming Zhang: College of Information Science and Engineering, Guilin University of Technology, Guilin 541006, China
Hongyan Wang: School of Software Engineering, University of Science and Technology of China, Hefei 230026, China

Sustainability, 2023, vol. 15, issue 2, 1-18

Abstract: With the development of education informatization and the accumulation of massive educational resources and teaching data in urban environments, educational knowledge graphs that provide good conditions for developing data-driven intelligent education have been proposed. Based on such educational knowledge graphs, the question-answering method can provide students with immediate coaching and significantly increase their learning interest and productivity. However, there is little research on knowledge graph question-answering focused on the educational field. Students tend to consult complex questions that require reasoning; however, the existing QA system cannot satisfy their complex information needs. To help improve sustainable learning efficiency, we propose a novel intelligent question-answering model applied in smart cities, which can reason over the educational knowledge graph to locate the answers to given questions. Our approach uses a highly expressive bilinear graph neural network technology to perform forward reasoning, utilizing the contextual information between graph nodes to improve reasoning ability. On this basis, we propose two-teacher knowledge distillation. We construct two distinct teacher networks by combining forward and backward reasoning, then incorporate the intermediate supervision signals from the two networks to guide the reasoning process, thereby mitigating the phenomenon of spurious path reasoning. Extensive experiments on the MOOC Q&A dataset prove the effectiveness of our approach.

Keywords: sustainable learning; intelligent question answering; educational knowledge graph; bilinear graph neural network; smart cities (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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